{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy,pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = arcpy.da.FeatureClassToNumPyArray(r\"E:\\work\\地统计插值\\weather.shp\",\n",
    "                                       [\"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "q25 = numpy.percentile(dt[\"mean\"], 25)\n",
    "q50 = numpy.percentile(dt[\"mean\"], 50)\n",
    "q75 = numpy.percentile(dt[\"mean\"], 75)\n",
    "iqr = q75 - q25\n",
    "u = q75 + 1.5 * iqr\n",
    "d = q25 - 1.5 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "if u > max(dt[\"mean\"]):\n",
    "    u =  max(dt[\"mean\"])\n",
    "if d < min(dt[\"mean\"]):\n",
    "    d = min(dt[\"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,8))\n",
    "plt.boxplot(dt[\"mean\"])\n",
    "plt.text(1.1,u,\"上边缘\")\n",
    "plt.text(1.1,d,\"下边缘\")\n",
    "plt.text(1.1,q25,\"下四分位（25%）\")\n",
    "plt.text(1.1,q50,\"中四分位（50%）\")\n",
    "plt.text(1.1,q75,\"中四分位（75%）\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
